For many scientists, the study of large datasets is an ongoing challenge. Numerical analyses are used to study a wide range of disciplines from computational physics and astronomical calculations to human economic systems. As part of their study, scientists often desire to visually represent their data cloud using computer generated imagery (CGI).
The visualization of this data using CGI is important because it allows scientists to rapidly draw conclusions from the data and to enhance their general cognition. Because of the potential benefits, a variety of visualization systems have been developed to convert three-dimensional (3D) datasets into two-dimensional (2D) CGI that is displayed on standard computer monitors.
One data cloud visualization method is disclosed in U.S. Pat. No. 6,278,459. This technique, like many others, uses a volume-rendering technique that first parcels the 3D space into smaller volumes referred to as voxels. These voxels are mathematical entities that are assigned attributes for color and opacity based on a user defined criteria. When converted into 2D CGI for display, the voxel properties are used to create pixels that define the appearance. While this process provides a great deal of flexibility in how static datasets are displayed, it produces significant processing overhead.
Another prior art data visualization method is disclosed in U.S. Pat. No. 5,339,386. This visualization technique also takes a volumetric approach towards the rendering of data. While the Open Graphics Language (Open GL) volumetric method of the '386 patent is an effective technique for creating distance specific effects for smoke, fog, and other volumetric calculations that need to change as the viewpoint moves through the effected volume, it introduces processes not needed for scientific visualizations where visualizing the entire dataset simultaneously is required. Additionally, the technique disclosed in the patent is limited in the range of color and opacity available due to the opacity blending approach that is used.
To address the many challenges associated with traditional data cloud visualization techniques, a method is needed to interactively render large datasets that change over time with blended color and opacity using both polygons and 2D image maps in an Open GL environment.